NLP - Sentiment Analysis - Restaurant Reviews

# Connecting Google Drive with Google Colab from google.colab import drive drive.mount('/content/drive/')

# Importing essential libraries import numpy as np import pandas as pd

# Loading the dataset df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/Datasets/Restaurant_Reviews.tsv', delimiter='\t', quoting=3)

df.shape

df.columns

df.head()

# Importing essential libraries for performing Natural Language Processing on 'Restaurant_Reviews.tsv' dataset import nltk import re nltk.download('stopwords') from nltk.corpus import stopwords from nltk.stem.porter import PorterStemmer

# Cleaning the reviews corpus = [] for i in range(0,1000): # Cleaning special character from the reviews review = re.sub(pattern='[^a-zA-Z]',repl=' ', string=df['Review'][i]) # Converting the entire review into lower case review = review.lower() # Tokenizing the review by words review_words = review.split() # Removing the stop words review_words = [word for word in review_words if not word in set(stopwords.words('english'))] # Stemming the words ps = PorterStemmer() review = [ps.stem(word) for word in review_words] # Joining the stemmed words review = ' '.join(review) # Creating a corpus corpus.append(review)

corpus[0:10]

# Creating the Bag of Words model from sklearn.feature_extraction.text import CountVectorizer cv = CountVectorizer(max_features=1500) X = cv.fit_transform(corpus).toarray() y = df.iloc[:, 1].values

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

# Fitting Naive Bayes to the Training set from sklearn.naive_bayes import MultinomialNB classifier = MultinomialNB() classifier.fit(X_train, y_train)

# Predicting the Test set results y_pred = classifier.predict(X_test)

# Accuracy, Precision and Recall from sklearn.metrics import accuracy_score from sklearn.metrics import precision_score from sklearn.metrics import recall_score score1 = accuracy_score(y_test,y_pred) score2 = precision_score(y_test,y_pred) score3= recall_score(y_test,y_pred) print("---- Scores ----") print("Accuracy score is: {}%".format(round(score1*100,2))) print("Precision score is: {}".format(round(score2,2))) print("Recall score is: {}".format(round(score3,2)))

# Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred)

cm

# Plotting the confusion matrix import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline plt.figure(figsize = (10,6)) sns.heatmap(cm, annot=True, cmap="YlGnBu", xticklabels=['Negative', 'Positive'], yticklabels=['Negative', 'Positive']) plt.xlabel('Predicted values') plt.ylabel('Actual values')

# Hyperparameter tuning the Naive Bayes Classifier best_accuracy = 0.0 alpha_val = 0.0 for i in np.arange(0.1,1.1,0.1): temp_classifier = MultinomialNB(alpha=i) temp_classifier.fit(X_train, y_train) temp_y_pred = temp_classifier.predict(X_test) score = accuracy_score(y_test, temp_y_pred) print("Accuracy score for alpha={} is: {}%".format(round(i,1), round(score*100,2))) if score>best_accuracy: best_accuracy = score alpha_val = i print('--------------------------------------------') print('The best accuracy is {}% with alpha value as {}'.format(round(best_accuracy*100, 2), round(alpha_val,1)))

classifier = MultinomialNB(alpha=0.2) classifier.fit(X_train, y_train)

def predict_sentiment(sample_review): sample_review = re.sub(pattern='[^a-zA-Z]',repl=' ', string = sample_review) sample_review = sample_review.lower() sample_review_words = sample_review.split() sample_review_words = [word for word in sample_review_words if not word in set(stopwords.words('english'))] ps = PorterStemmer() final_review = [ps.stem(word) for word in sample_review_words] final_review = ' '.join(final_review) temp = cv.transform([final_review]).toarray() return classifier.predict(temp)

# Predicting values sample_review = 'The food is really good here.' if predict_sentiment(sample_review): print('This is a POSITIVE review.') else: print('This is a NEGATIVE review!')

# Predicting values sample_review = 'Food was pretty bad and the service was very slow.' if predict_sentiment(sample_review): print('This is a POSITIVE review.') else: print('This is a NEGATIVE review!')

# Predicting values sample_review = 'The food was absolutely wonderful, from preparation to presentation, very pleasing.' if predict_sentiment(sample_review): print('This is a POSITIVE review.') else: print('This is a NEGATIVE review!')